Atmospheric refractivity and its vertical changes play an important role in how radio waves travel, but detailed studies across different climate regions are still limited. This study provides a long-term analysis of refractivity, its vertical gradients, and k-factor changes across five major climate zones: tropical, arid, temperate, subarctic, and polar. Using several decades of atmospheric data, we analyse vertical structures, seasonal patterns, year-to-year changes, and extreme conditions, and relate them to physical processes in the atmosphere that affect radio-wave behaviour. The results show that tropical regions have the strongest negative refractivity gradients near the surface (– 54.6 N/km) and the highest occurrence of super-refraction, mainly due to the combined effects of high humidity and temperature, especially during wet-season convection. In contrast, arid and polar regions are mainly influenced by temperature, showing more frequent sub-refraction and weaker seasonal changes. Temperate and subarctic regions show mixed characteristics, influenced by both moisture and temperature layering. In addition, we provide a direct evidence for the influence of large-scale climate variability, particularly El Niño, on refractivity gradients. Specifically, we examined the relationship between the refractivity gradient and the Niño 3.4 Index, and a clear correspondence between the temporal variations of the refractivity gradient and the Niño 3.4 Index is observed, providing independent support for our interpretation and strengthening the link between ENSO-related climate variability and refractivity gradient changes. We also demonstrate the effect of surface-pressure masking on refractivity gradient estimates. These findings highlight the need for climate-specific refractivity models and provide useful guidance for improving radio-wave prediction and communication system design in different environmental settings.

Water vapour is known to represent the amount of water in the atmosphere. However, periodic and regular variations in both radio refractive index (N) and saturated water vapour density (SWVD) were observed at selected Nigerian stations. This investigation used monthly air temperature, air pressure, and relative humidity data from four stations (Iwo, Ife, Ilesha, and Osogbo) collected from the soda (http://www.soda-pro.com) HelioClim website repository of MERRA-2 meteorological re-analysis data. However, for the research, there have been factors influencing radio signal transmission that operate in the troposphere, contributing to saturated water vapour. The result of the research showed that analysis of monthly MERRA-2 data indicated that N and SWVD peak in May (379 N, 20.50 g/m 3) and trough in January (333 N, 14.00 g/m 3). The results showed a strong seasonal dependence, with N and SWVD increasing by 13.5% and 46.4%, respectively, from dry to wet season. These findings have implications for optimizing radio system performance in Sub-Saharan Africa. Present study also revealed that radio refractive index and SWVD are generally higher in the wet season and lower in the dry season at the selected stations. As a result, the study concluded that in order to achieve good radio refractive index performance in Sub-Saharan Africa, the country must account for the variability of the meteorological parameters used in the study of radio refractive index and SWVD for the design of optimal system performance.

The assessment of wind energy potential is essential for sustainable energy planning, particularly in Africa, where renewable energy sources are increasingly considered for power generation. This study evaluates the wind energy potential of eight selected African stations Nairobi (Kenya), Addis Ababa (Ethiopia), Cairo (Egypt), Tunis (Tunisia), Abuja (Nigeria), Accra (Ghana), Pretoria (South Africa), and Luanda (Angola) using wind speed data from 2000 to 2023 obtained from NASA’s power data access viewer. The Weibull probability distribution function (PDF) and cumulative distribution function (CDF) were applied to characterize wind speed variations, employing five different parameter estimation methods. The results indicate significant spatial variability in wind characteristics across the stations. The mean wind speed ranged from 2.31 m/s in Accra, Ghana, to 6.82 m/s in Nairobi, Kenya, highlighting substantial differences in wind energy potential. The shape parameter (k) varied between 1.79 in Luanda and 2.31 in Addis Ababa, while the scale parameter (c) ranged from 2.91 m/s in Accra to 7.48 m/s in Nairobi. Wind power density estimates revealed that Nairobi exhibited the highest power density of 248.7 W/m², followed by Addis Ababa (198.3 W/m²), indicating strong wind energy potential. In contrast, Accra had the lowest power density (34.6 W/m²), suggesting limited viability for wind power generation. Seasonal variations showed that wind speeds peaked in the dry season and declined during wet periods across most locations. These findings provide valuable insights into wind energy feasibility in Africa, aiding policy makers in site selection for wind power development and contributing to the continent’s transition toward renewable energy sources.